1. Field of the Invention
The embodiments of the invention generally relate to automated manufacturing process control, and more particularly to modeling a manufacturing system through intelligent automated reticle management.
2. Description of the Related Art
Semiconductor manufacturing involves several hundred detailed and complex processes, which must be skillfully coordinated according to stringent fabrication schedules. Semiconductor manufacturing processes may include photolithography processes, etching processes, deposition processes, polishing processes, rapid thermal processes, implantation processes, annealing processes, among others. As such, specific machines and tools are required to perform the above-mentioned processes according to defined manufacturing rules.
One of the individual processes creating significant bottlenecks in the overall manufacturing process is the photolithography process. While solutions exist to aid in improving the efficiency of the photolithography process, there remain delays associated with the scheduling of auxiliary equipment called reticles (Park, S. et al., “Assessment of Potential Gains in Productivity Due to Proactive Reticle Management Using Discrete Event Simulation,” Proceedings of the 1999 Winter Simulation Conference, eds. Farrington, P. A., Nembhard, H. B., Sturrock, D. T., and Evans, G. W., pp. 856–864, the complete disclosure of which is herein incorporated by reference).
Reticles are auxiliary devices that are used for projecting complex circuit patterns onto the surface of a wafer by way of a photolithographic process. As such, reticles are components associated with the machines or tools used in the specific manufacturing processes, and thus different reticles must be allocated to machines or tools depending on the manufacturing process to be carried out. Moreover, there may be several reticles that could be allocated to one particular machine or tool at a given time, and hence there is a need to optimize reticle management decisions.
Computerized simulations exist to provide modeling guidelines for identifying bottlenecks in a manufacturing process and for simulating solutions to predict and mitigate these bottlenecks, wherein some of these simulations focus on automated material handling systems (AMHS), which evaluate work-in-process (WIP) projections of workpiece lots through the manufacturing process (Nadoli, G. et al., “Simulation in Automated Material Handling Systems Design for Semiconductor Manufacturing,” Proceedings of the 1994 Winter Simulation Conference, eds. Tew, J. D., Manivannan, S., Sadowski, D. A., and Seila, A. F., pp. 892–899, the complete disclosure of which is herein incorporated by reference). Moreover, a productive manufacturing process may be defined by, among other factors, product throughput, which is the rate at which the process produces product output.
Because of the complexities involved in semiconductor manufacturing, there remains a need for improving and controlling the manufacturing process. Moreover, improved modeling techniques are needed to better address issues such as reticle dispatching to relieve bottlenecks which impact manufacturing efficiency.